Papers by Shuyu Zhang

6 papers
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)

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Challenge: Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training.
Approach: They propose a framework that enhances zero-shot slot inference through robust prompt alignment.
Outcome: Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST.
Enhancing RAG Efficiency with Adaptive Context Compression (2025.findings-emnlp)

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Challenge: Existing methods apply fixed compression rates, over-compressing simple queries or under-compressed complex ones.
Approach: a new framework uses a hierarchical compressor and a context selector to optimize inference efficiency . a framework that dynamically adjusts compression rates based on input complexity optimizes inference without loss of accuracy.
Outcome: Adaptive Context Compression for RAG outperforms fixed-rate methods on Wikipedia and five QA datasets .
DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)

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Challenge: Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions .
Approach: They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework.
Outcome: The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution.
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)

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Challenge: Recent years have witnessed rapid advances in text-to-music generation using large language models.
Approach: They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content .
Outcome: The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio.
DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)

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Challenge: Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts.
Approach: They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C.
Outcome: The proposed framework achieves SOTA performance in success rate, efficiency, and generalization.
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models (2025.findings-acl)

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Challenge: Recent years have witnessed significant advancements in large language models (LLMs) but still struggle with integrating vision and audio.
Approach: They propose a self-knowledge distillation method to improve vision-audio capabilities of OLLMs by learning from the vision-text components.
Outcome: The proposed method improves vision-audio capabilities of OLLMs by learning from vision-text components, which improves interaction between audio and images and results in improved performance on multimodal tasks.

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